{
 "cells": [
  {
   "cell_type": "raw",
   "id": "a9c616eb",
   "metadata": {},
   "source": [
    "机器学习实战 利用 sklearn 库预测科比生涯数据\n",
    "\n",
    "https://www.freesion.com/article/2210309261/\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a7b128ed",
   "metadata": {
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "这个表格记录了科比30000多个镜头的详细数据，共有25个标签。\n",
    "\n",
    "具体的设计思路是将这25个标签代表的数据进行分析，找出对科比投篮结果有影响的标签，利用机器学习中随机森林的算法训练出可以预测科比是否能够投篮命中的模型。\n",
    "\n",
    "先来看看这25个标签具体代表什么(自己不是篮球的专业人士和爱好者，所以具体的内容可能有所出入，不过不会影响到分析结果)\n",
    "\n",
    "action_type（用什么方式投的篮）\n",
    "combined_shot_type（结合什么方式投篮）\n",
    "game_event_id（游戏事件ID）\n",
    "game_id（游戏ID）\n",
    "la（投篮的经度）\n",
    "loc_x （投篮的x坐标）\n",
    "loc_y（投篮的y坐标）\n",
    "lon（投篮的纬度）\n",
    "minutes_remaining（离比赛结束还有多少分钟）\n",
    "period（第几场）\n",
    "playoffs（是不是季后赛）\n",
    "season（赛季）\n",
    "seconds_remaining（离比赛结束还有多少秒）\n",
    "shot_distance（投篮离篮筐的的距离）\n",
    "shot_made_flag （是不是进球了(主要的标签)）\n",
    "shot_type（2分球还是3分球区域）\n",
    "shot_zone_area（投篮区域的表示方法一）\n",
    "shot_zone_basic（投篮区域的表示方法二）\n",
    "shot_zone_range（投篮区域的表示方法三）\n",
    "team_id（队伍ID）\n",
    "team_name（队伍名字）\n",
    "game_date（比赛时间）\n",
    "matchup（比赛双方队伍）\n",
    "opponent（自己所在队伍名字）\n",
    "shot_id（镜头ID）\n",
    "可以看到，这25个标签中对于科比能否投篮命中有一些无关紧要的数据，比如team_id，因为这30000多份样本中全是在湖人队打的，shot_id，game_id等等这个数据也无关紧要，具体的分析将会在下面讲解。"
   ]
  },
  {
   "cell_type": "raw",
   "id": "04a65617",
   "metadata": {
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "第一步：数据预处理与特征工程：\n",
    "删除缺失或重复记录，统一时间与坐标系统；\n",
    "将投篮位置坐标转换为极坐标；\n",
    "新增衍生变量包括时间变量（剩余时间、节次、是否加时）、空间变量（距离、角度区域）、\n",
    "比赛背景变量（是否主场、季后赛、分差）、心理压力代理变量（剩余时间<24秒、比分落后投篮）;\n",
    "归一化数值变量并独热编码类别变量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf08d0d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30697, 25)\n",
      "         action_type combined_shot_type  game_event_id   game_id      lat  \\\n",
      "0          Jump Shot          Jump Shot             10  20000012  33.9723   \n",
      "1          Jump Shot          Jump Shot             12  20000012  34.0443   \n",
      "2          Jump Shot          Jump Shot             35  20000012  33.9093   \n",
      "3          Jump Shot          Jump Shot             43  20000012  33.8693   \n",
      "4  Driving Dunk Shot               Dunk            155  20000012  34.0443   \n",
      "\n",
      "   loc_x  loc_y       lon  minutes_remaining  period  ...       shot_type  \\\n",
      "0    167     72 -118.1028                 10       1  ...  2PT Field Goal   \n",
      "1   -157      0 -118.4268                 10       1  ...  2PT Field Goal   \n",
      "2   -101    135 -118.3708                  7       1  ...  2PT Field Goal   \n",
      "3    138    175 -118.1318                  6       1  ...  2PT Field Goal   \n",
      "4      0      0 -118.2698                  6       2  ...  2PT Field Goal   \n",
      "\n",
      "          shot_zone_area  shot_zone_basic  shot_zone_range     team_id  \\\n",
      "0          Right Side(R)        Mid-Range        16-24 ft.  1610612747   \n",
      "1           Left Side(L)        Mid-Range         8-16 ft.  1610612747   \n",
      "2   Left Side Center(LC)        Mid-Range        16-24 ft.  1610612747   \n",
      "3  Right Side Center(RC)        Mid-Range        16-24 ft.  1610612747   \n",
      "4              Center(C)  Restricted Area  Less Than 8 ft.  1610612747   \n",
      "\n",
      "            team_name   game_date    matchup opponent  shot_id  \n",
      "0  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        1  \n",
      "1  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        2  \n",
      "2  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        3  \n",
      "3  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        4  \n",
      "4  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        5  \n",
      "\n",
      "[5 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 导入数据\n",
    "filename= \"data.csv\"\n",
    "raw = pd.read_csv(filename)\n",
    "print(raw.shape)\n",
    "print(raw.head())  #head函数打印前5行，如果需要打印前10行，这样写"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09848422",
   "metadata": {},
   "source": [
    "接下来我们再来分析这一份数据表，我们发现其中 shot_made_flag 这个标签竟然有缺失值，这个表示了科比是否进球了，作为最重要的数据，是不能随意进行填充的，我们必须删除掉这些样本进行下一步的工作，代码如下\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba8befb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25697, 25)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 导入数据\n",
    "filename= \"data.csv\"\n",
    "raw = pd.read_csv(filename)\n",
    "\n",
    "kobe =  raw[pd.notnull(raw['shot_made_flag'])]\n",
    "print(kobe.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298e278f",
   "metadata": {},
   "source": [
    "此时我们只有 25697 个数据进行训练了。\n",
    "\n",
    "接着我们分析 lat，loc_x，loc_y，lon 这 4 个标签，这 4 个标签说明了科比投篮的位置，而具体指的是什么呢，有什么关系吗，我们画散点图来看一下。\n",
    "\n",
    "编写代码如下\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1dc74409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 导入数据\n",
    "filename= \"data.csv\"\n",
    "raw = pd.read_csv(filename)\n",
    "\n",
    "\n",
    "#删除shot_made_flag为空的数据项，并且命名为kobe用作训练\n",
    "kobe =  raw[pd.notnull(raw['shot_made_flag'])]\n",
    "\n",
    "\n",
    "\n",
    "#画散点图用来分析lat loc_x  loc_y lon这4个标签\n",
    "alpha = 0.02   #指定一个数字，用于后面的透明度\n",
    "plt.figure(figsize=(6,6))  #指定画图域\n",
    "# loc_x and loc_y\n",
    "plt.subplot(121)    #一行两列   第一个位置\n",
    "plt.scatter(kobe.loc_x, kobe.loc_y, color='red', alpha=alpha)   #画散点图\n",
    "plt.title('loc_x and loc_y')\n",
    "# lat and lon\n",
    "plt.subplot(122)    #一行两列   第二个位置\n",
    "plt.scatter(kobe.lon, kobe.lat, color='blue', alpha=alpha)\n",
    "plt.title('lat and lon')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48a9f77",
   "metadata": {},
   "source": [
    "我们大致可以看出，这 4 个坐标大致表示了距离篮筐的距离，那样的话，我们接下来用于数据处理的时候选择其中的一组数据即可了。\n",
    "\n",
    "shot_type，shot_zone_area，shot_zone_basic，shot_zone_range 这 4 个标签代表了投篮的区域，其实还是说明一件事，这里就不做赘述了，当然 shot_zone_area，shot_zone_basic，shot_zone_range 这 3 个标签将投篮区域相比于 shot_type 来说分的更细，直接删掉是不是会有问题，其实大可不必担心，因为，接下来我们将会用极坐标的形式表示科比的投篮位置，将更会细化科比的投篮区域。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54bcc411",
   "metadata": {},
   "source": [
    "数据处理\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc092ee",
   "metadata": {},
   "source": [
    "首先处理我们上节所说的极坐标的问题，然后我们会发现算出来的 dist 和，shot_distance 竟然具有正相关性。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be81a24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from sklearn.ensemble import RandomForestClassifier  #导入随机森林分类器\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "# 导入数据\n",
    "filename= \"data.csv\"\n",
    "raw = pd.read_csv(filename)\n",
    "\n",
    "\n",
    "#删除shot_made_flag为空的数据项，并且命名为kobe用作训练\n",
    "kobe =  raw[pd.notnull(raw['shot_made_flag'])]\n",
    "\n",
    "\n",
    "#对于lat，loc_x，loc_y，lon这4个标签，我们取loc_x和loc_y这2个标签，并将其转化为极坐标的形式\n",
    "#dist表示离篮筐的距离，angle表示投篮的角度，这样将会更好的科比投篮的反应结果`\n",
    "raw['dist'] = np.sqrt(raw['loc_x']**2 + raw['loc_y']**2)\n",
    "loc_x_zero = raw['loc_x'] == 0\n",
    "# initialize angle column safely and assign using .loc to avoid chained-assignment warnings\n",
    "raw['angle'] = np.zeros(len(raw))\n",
    "raw.loc[~loc_x_zero, 'angle'] = np.arctan(raw.loc[~loc_x_zero, 'loc_y'] / raw.loc[~loc_x_zero, 'loc_x'])\n",
    "raw.loc[loc_x_zero, 'angle'] = np.pi / 2\n",
    "\n",
    "#画图展示dist和shot_distance的正相关性\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(raw.dist, raw.shot_distance, color='blue')\n",
    "plt.title('dist and shot_distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a34529",
   "metadata": {},
   "source": [
    "这样我们可以保留其中的一个(这里我们保留了 dist 这个标签)，接着我们将 minutes_remaining 和 seconds_remaining 转化成一个标签 remaining_time，然后删除不必要的列，非数值型的转换成 onehot 编码格式\n",
    "\n",
    "具体编写代码如下，具体说明在代码注释中\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "693437b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   playoffs  shot_made_flag        dist     angle  remaining_time  \\\n",
      "0         0             NaN  181.859836  0.407058             627   \n",
      "1         0             0.0  157.000000 -0.000000             622   \n",
      "2         0             1.0  168.600119 -0.928481             465   \n",
      "3         0             0.0  222.865430  0.903063             412   \n",
      "4         0             1.0    0.000000  1.570796             379   \n",
      "\n",
      "   action_type_Alley Oop Dunk Shot  action_type_Alley Oop Layup shot  \\\n",
      "0                            False                             False   \n",
      "1                            False                             False   \n",
      "2                            False                             False   \n",
      "3                            False                             False   \n",
      "4                            False                             False   \n",
      "\n",
      "   action_type_Cutting Finger Roll Layup Shot  action_type_Cutting Layup Shot  \\\n",
      "0                                       False                           False   \n",
      "1                                       False                           False   \n",
      "2                                       False                           False   \n",
      "3                                       False                           False   \n",
      "4                                       False                           False   \n",
      "\n",
      "   action_type_Driving Bank shot  ...  season_10  season_11  season_12  \\\n",
      "0                          False  ...      False      False      False   \n",
      "1                          False  ...      False      False      False   \n",
      "2                          False  ...      False      False      False   \n",
      "3                          False  ...      False      False      False   \n",
      "4                          False  ...      False      False      False   \n",
      "\n",
      "   season_13  season_14  season_15  season_16  season_97  season_98  season_99  \n",
      "0      False      False      False      False      False      False      False  \n",
      "1      False      False      False      False      False      False      False  \n",
      "2      False      False      False      False      False      False      False  \n",
      "3      False      False      False      False      False      False      False  \n",
      "4      False      False      False      False      False      False      False  \n",
      "\n",
      "[5 rows x 130 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 导入数据\n",
    "filename= \"data.csv\"\n",
    "raw = pd.read_csv(filename)\n",
    "\n",
    "# 1) 删除重复记录\n",
    "raw = raw.drop_duplicates().reset_index(drop=True)\n",
    "\n",
    "# 2) 统一时间字段（解析 game_date）\n",
    "if 'game_date' in raw.columns:\n",
    "    raw['game_date'] = pd.to_datetime(raw['game_date'], errors='coerce')\n",
    "    # 可以从 game_date 提取年/月/日做为特征（可选）\n",
    "    raw['game_year'] = raw['game_date'].dt.year\n",
    "    raw['game_month'] = raw['game_date'].dt.month\n",
    "else:\n",
    "    raw['game_year'] = np.nan\n",
    "    raw['game_month'] = np.nan\n",
    "\n",
    "# 删除 shot_made_flag 为空的数据项并命名为 kobe 用作训练（保留 raw 用于后续构造 test_shot_id）\n",
    "kobe = raw[pd.notnull(raw['shot_made_flag'])].copy()\n",
    "\n",
    "# 3) 将 (loc_x, loc_y) 转成极坐标：dist, angle（使用 arctan2 处理象限）\n",
    "raw['dist'] = np.sqrt(raw['loc_x']**2 + raw['loc_y']**2)\n",
    "raw['angle'] = np.arctan2(raw['loc_y'], raw['loc_x'])\n",
    "# 将 angle 归一到 [0, 2*pi)（如果需要）\n",
    "raw['angle'] = raw['angle'].apply(lambda a: a if a >= 0 else a + 2*np.pi)\n",
    "\n",
    "# 4) 衍生时间相关变量\n",
    "# remaining_time（秒）\n",
    "raw['remaining_time'] = raw['minutes_remaining'] * 60 + raw['seconds_remaining']\n",
    "# 是否为加时（period>4 一般表示加时）\n",
    "raw['is_overtime'] = pd.to_numeric(raw['period'], errors='coerce').fillna(0) > 4\n",
    "\n",
    "# 5) 比赛背景变量与心理压力代理变量\n",
    "# 是否主场：matchup 中包含 '@' 表示客场；不包含表示主场（根据 Kaggle 原始约定）\n",
    "if 'matchup' in raw.columns:\n",
    "    raw['is_home'] = ~raw['matchup'].str.contains('@', na=False)\n",
    "else:\n",
    "    raw['is_home'] = np.nan\n",
    "\n",
    "# 剩余时间小于 24 秒作为压力代理\n",
    "raw['pressure_24s'] = raw['remaining_time'] < 24\n",
    "\n",
    "# 6) 空间角度分桶（示例：将角度分成 8 个扇区）\n",
    "raw['angle_bucket'] = pd.cut(raw['angle'], bins=8, labels=False, include_lowest=True)\n",
    "\n",
    "# 7) 赛季解析（已做空值保护）\n",
    "raw['season'] = raw['season'].apply(lambda x: int(x.split('-')[1]) if pd.notnull(x) else x)\n",
    "\n",
    "# 8) 删除对比赛结果无关的列（保留 shot_id 直到我们保存 test_shot_id）\n",
    "drops = ['team_id', 'team_name', 'shot_zone_area', 'shot_zone_range', 'shot_zone_basic', 'lon',\n",
    "         'lat', 'seconds_remaining', 'minutes_remaining', 'shot_distance', 'loc_x', 'loc_y', 'game_event_id', 'game_id']\n",
    "for drop in drops:\n",
    "    if drop in raw.columns:\n",
    "        raw = raw.drop(drop, axis=1)\n",
    "\n",
    "# 9) 把非数值型变量 one-hot 编码（包含我们新增的 angle_bucket, is_overtime, is_home, pressure_24s）\n",
    "categorical_vars = [v for v in ['action_type', 'combined_shot_type', 'shot_type', 'opponent', 'period', 'season', 'angle_bucket', 'is_overtime', 'is_home', 'pressure_24s'] if v in raw.columns]\n",
    "for var in categorical_vars:\n",
    "    dummies = pd.get_dummies(raw[var].astype(str), prefix=var)\n",
    "    raw = pd.concat([raw, dummies], axis=1)\n",
    "    raw = raw.drop(var, axis=1)\n",
    "\n",
    "print('预处理完成，示例数据：')\n",
    "print(raw.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f870059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "playoffs",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "shot_made_flag",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "dist",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "angle",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "remaining_time",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "action_type_Alley Oop Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Alley Oop Layup shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Cutting Finger Roll Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Cutting Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Bank shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Finger Roll Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Finger Roll Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Floating Bank Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Floating Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Hook Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Jump shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Reverse Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Driving Slam Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Fadeaway Bank shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Fadeaway Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Finger Roll Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Finger Roll Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Floating Jump shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Follow Up Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Hook Bank Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Hook Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Jump Bank Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Jump Hook Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Pullup Bank shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Pullup Jump shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Putback Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Putback Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Putback Slam Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Reverse Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Reverse Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Reverse Slam Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Bank shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Finger Roll Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Finger Roll Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Hook Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Pull-Up Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Reverse Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Slam Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Running Tip Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Slam Dunk Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Step Back Jump shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Tip Layup Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Tip Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Turnaround Bank shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Turnaround Fadeaway Bank Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Turnaround Fadeaway shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Turnaround Finger Roll Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Turnaround Hook Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "action_type_Turnaround Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "combined_shot_type_Bank Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "combined_shot_type_Dunk",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "combined_shot_type_Hook Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "combined_shot_type_Jump Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "combined_shot_type_Layup",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "combined_shot_type_Tip Shot",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "shot_type_2PT Field Goal",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "shot_type_3PT Field Goal",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_ATL",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_BKN",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_BOS",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_CHA",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_CHI",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_CLE",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_DAL",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_DEN",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_DET",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_GSW",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_HOU",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_IND",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_LAC",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_MEM",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_MIA",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_MIL",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_MIN",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_NJN",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_NOH",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_NOP",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_NYK",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_OKC",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_ORL",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_PHI",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_PHX",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_POR",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_SAC",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_SAS",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_SEA",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_TOR",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_UTA",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_VAN",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "opponent_WAS",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "period_1",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "period_2",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "period_3",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "period_4",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "period_5",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "period_6",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "period_7",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_0",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_1",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_2",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_3",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_4",
         "rawType": "bool",
         "type": "boolean"
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        {
         "name": "season_5",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_6",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_7",
         "rawType": "bool",
         "type": "boolean"
        },
        {
         "name": "season_8",
         "rawType": "bool",
         "type": "boolean"
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        {
         "name": "season_9",
         "rawType": "bool",
         "type": "boolean"
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        {
         "name": "season_10",
         "rawType": "bool",
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        {
         "name": "season_11",
         "rawType": "bool",
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        {
         "name": "season_12",
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        {
         "name": "season_13",
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        {
         "name": "season_14",
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        {
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        {
         "name": "season_97",
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        {
         "name": "season_98",
         "rawType": "bool",
         "type": "boolean"
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        {
         "name": "season_99",
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       "   playoffs  shot_made_flag        dist     angle  remaining_time  \\\n",
       "0         0             NaN  181.859836  0.407058             627   \n",
       "1         0             0.0  157.000000 -0.000000             622   \n",
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       "\n",
       "   action_type_Alley Oop Dunk Shot  action_type_Alley Oop Layup shot  \\\n",
       "0                            False                             False   \n",
       "1                            False                             False   \n",
       "2                            False                             False   \n",
       "3                            False                             False   \n",
       "4                            False                             False   \n",
       "\n",
       "   action_type_Cutting Finger Roll Layup Shot  action_type_Cutting Layup Shot  \\\n",
       "0                                       False                           False   \n",
       "1                                       False                           False   \n",
       "2                                       False                           False   \n",
       "3                                       False                           False   \n",
       "4                                       False                           False   \n",
       "\n",
       "   action_type_Driving Bank shot  ...  season_10  season_11  season_12  \\\n",
       "0                          False  ...      False      False      False   \n",
       "1                          False  ...      False      False      False   \n",
       "2                          False  ...      False      False      False   \n",
       "3                          False  ...      False      False      False   \n",
       "4                          False  ...      False      False      False   \n",
       "\n",
       "   season_13  season_14  season_15  season_16  season_97  season_98  season_99  \n",
       "0      False      False      False      False      False      False      False  \n",
       "1      False      False      False      False      False      False      False  \n",
       "2      False      False      False      False      False      False      False  \n",
       "3      False      False      False      False      False      False      False  \n",
       "4      False      False      False      False      False      False      False  \n",
       "\n",
       "[5 rows x 130 columns]"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6811dc",
   "metadata": {},
   "source": [
    "为什么会有 129 行之多，是因为我们用了 onehot 编码，具体什么是 onehot 编码这里就不做赘述了，感兴趣的可以谷歌或者百度一下。\n",
    "\n",
    "最后我们总结一下，到底这 25 个标签还剩下什么，首先除去和比赛结果无关的标签，’shot_id’, ‘team_id’, ‘team_name’, ‘shot_zone_area’, ‘shot_zone_range’, ‘shot_zone_basic’,’matchup’, ‘lon’,\n",
    "‘lat’, ‘seconds_remaining’, ‘minutes_remaining’，’shot_distance’, , ‘game_event_id’, ‘game_id’,\n",
    "‘game_date’\n",
    "\n",
    "然后’loc_x’, ‘loc_y’转换成了极坐标的形式，变成了’dist’,’angle’;’seconds_remaining’和’minutes_remaining’合并成了’remaining_time’。\n",
    "\n",
    "最后将’action_type’, ‘combined_shot_type’, ‘shot_type’, ‘opponent’, ‘period’, ‘season’转换成 onehot 编码格式。\n",
    "\n",
    "至此我们的数据处理工作基本完成了。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ec771d",
   "metadata": {},
   "source": [
    "利用 sklearn 来进行数据的处理\n",
    "\n",
    "具体的思路是利用随机森林分类器配合着交叉验证的方法进行数据的分析，先找到最佳的树的个数，和树的深度。\n",
    "\n",
    "编写代码如下\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74281b2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "寻找随机森林分类器的的最佳树的数量...\n",
      "树的数量 : 1\n",
      "建造 1 颗树(耗时0.757秒)\n",
      "树的数量 : 1\n",
      "建造 1 颗树(耗时0.757秒)\n",
      "树的数量 : 1\n",
      "建造 1 颗树(耗时0.732秒)\n",
      "树的数量 : 2\n",
      "建造 1 颗树(耗时0.732秒)\n",
      "树的数量 : 2\n",
      "建造 2 颗树(耗时1.143秒)\n",
      "树的数量 : 4\n",
      "建造 2 颗树(耗时1.143秒)\n",
      "树的数量 : 4\n",
      "建造 4 颗树(耗时2.071秒)\n",
      "树的数量 : 7\n",
      "建造 4 颗树(耗时2.071秒)\n",
      "树的数量 : 7\n",
      "建造 7 颗树(耗时3.433秒)\n",
      "树的数量 : 12\n",
      "建造 7 颗树(耗时3.433秒)\n",
      "树的数量 : 12\n",
      "建造 12 颗树(耗时6.210秒)\n",
      "树的数量 : 21\n",
      "建造 12 颗树(耗时6.210秒)\n",
      "树的数量 : 21\n",
      "建造 21 颗树(耗时10.295秒)\n",
      "树的数量 : 35\n",
      "建造 21 颗树(耗时10.295秒)\n",
      "树的数量 : 35\n",
      "建造 35 颗树(耗时16.978秒)\n",
      "树的数量 : 59\n",
      "建造 35 颗树(耗时16.978秒)\n",
      "树的数量 : 59\n",
      "建造 59 颗树(耗时26.530秒)\n",
      "树的数量 : 100\n",
      "建造 59 颗树(耗时26.530秒)\n",
      "树的数量 : 100\n",
      "建造 100 颗树(耗时44.788秒)\n",
      "最佳树的颗树为 : 100,得分为: 12.360084141310487\n",
      "\n",
      "\n",
      "寻找随机森林分类器的最佳树的最佳深度...\n",
      "树的最大的深度 : 1\n",
      "建造 100 颗树(耗时44.788秒)\n",
      "最佳树的颗树为 : 100,得分为: 12.360084141310487\n",
      "\n",
      "\n",
      "寻找随机森林分类器的最佳树的最佳深度...\n",
      "树的最大的深度 : 1\n",
      "树的最大深度为: 1(耗时3.292秒)\n",
      "树的最大的深度 : 1\n",
      "树的最大深度为: 1(耗时3.292秒)\n",
      "树的最大的深度 : 1\n",
      "树的最大深度为: 1(耗时3.277秒)\n",
      "树的最大的深度 : 2\n",
      "树的最大深度为: 1(耗时3.277秒)\n",
      "树的最大的深度 : 2\n",
      "树的最大深度为: 2(耗时4.395秒)\n",
      "树的最大的深度 : 4\n",
      "树的最大深度为: 2(耗时4.395秒)\n",
      "树的最大的深度 : 4\n",
      "树的最大深度为: 4(耗时6.705秒)\n",
      "树的最大的深度 : 7\n",
      "树的最大深度为: 4(耗时6.705秒)\n",
      "树的最大的深度 : 7\n",
      "树的最大深度为: 7(耗时10.111秒)\n",
      "树的最大的深度 : 12\n",
      "树的最大深度为: 7(耗时10.111秒)\n",
      "树的最大的深度 : 12\n",
      "树的最大深度为: 12(耗时16.057秒)\n",
      "树的最大的深度 : 21\n",
      "树的最大深度为: 12(耗时16.057秒)\n",
      "树的最大的深度 : 21\n",
      "树的最大深度为: 21(耗时26.747秒)\n",
      "树的最大的深度 : 35\n",
      "树的最大深度为: 21(耗时26.747秒)\n",
      "树的最大的深度 : 35\n",
      "树的最大深度为: 35(耗时39.293秒)\n",
      "树的最大的深度 : 59\n",
      "树的最大深度为: 35(耗时39.293秒)\n",
      "树的最大的深度 : 59\n",
      "树的最大深度为: 59(耗时50.606秒)\n",
      "树的最大的深度 : 100\n",
      "树的最大深度为: 59(耗时50.606秒)\n",
      "树的最大的深度 : 100\n",
      "树的最大深度为: 100(耗时51.242秒)\n",
      "最佳树的深度: 12,得分为：11.44697075810712\n",
      "树的最大深度为: 100(耗时51.242秒)\n",
      "最佳树的深度: 12,得分为：11.44697075810712\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from sklearn.ensemble import RandomForestClassifier  #导入随机森林分类器\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "# 导入数据\n",
    "filename= \"data.csv\"\n",
    "raw = pd.read_csv(filename)\n",
    "\n",
    "# 在删除列之前保存测试集的 shot_id 以便最后输出结果（避免 NameError）\n",
    "test_shot_id = raw.loc[pd.isnull(raw['shot_made_flag']), ['shot_id']].copy()\n",
    "\n",
    "#删除shot_made_flag为空的数据项，并且命名为kobe用作训练\n",
    "kobe =  raw[pd.notnull(raw['shot_made_flag'])]\n",
    "\n",
    "\n",
    "\n",
    "#对于lat，loc_x，loc_y，lon这4个标签，我们取loc_x和loc_y这2个标签，并将其转化为极坐标的形式\n",
    "#dist表示离篮筐的距离，angle表示投篮的角度，这样将会更好的科比投篮的反应结果`\n",
    "raw['dist'] = np.sqrt(raw['loc_x']**2 + raw['loc_y']**2)\n",
    "loc_x_zero = raw['loc_x'] == 0\n",
    "# initialize angle column safely and assign using .loc to avoid chained-assignment warnings\n",
    "raw['angle'] = np.zeros(len(raw))\n",
    "raw.loc[~loc_x_zero, 'angle'] = np.arctan(raw.loc[~loc_x_zero, 'loc_y'] / raw.loc[~loc_x_zero, 'loc_x'])\n",
    "raw.loc[loc_x_zero, 'angle'] = np.pi / 2\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 对于minutes_remaining：离比赛结束还有多少分钟；seconds_remaining：离比赛结束还有多少秒（0-60），这\n",
    "# 2个属性我们合成距离比赛结束的时间\n",
    "raw['remaining_time'] = raw['minutes_remaining'] * 60 + raw['seconds_remaining']\n",
    "\n",
    "\n",
    "\n",
    "#机器学习只能识别数值型的数据\n",
    "#将赛季中'Jan-00' 'Feb-01' 'Mar-02'  ···  '1998-99'转换成\n",
    "# 0  1  2  ··· 99\n",
    "raw['season'] = raw['season'].apply(lambda x: int(x.split('-')[1]) if pd.notnull(x) else x)\n",
    "\n",
    "\n",
    "\n",
    "# 删除对于比赛结果没有影响的数据\n",
    "drops = ['shot_id', 'team_id', 'team_name', 'shot_zone_area', 'shot_zone_range', 'shot_zone_basic','matchup', 'lon',\n",
    "         'lat', 'seconds_remaining', 'minutes_remaining','shot_distance', 'loc_x', 'loc_y', 'game_event_id', 'game_id',\n",
    "         'game_date']\n",
    "for drop in drops:\n",
    "    raw = raw.drop(drop, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "#将非数值型的数据转换成为onehot编码的格式，加入到数据中并且将原来的数据删除\n",
    "categorical_vars = ['action_type', 'combined_shot_type', 'shot_type', 'opponent', 'period', 'season']\n",
    "for var in categorical_vars:\n",
    "    dummies = pd.get_dummies(raw[var], prefix=var)\n",
    "    raw = pd.concat([raw, dummies], axis=1)\n",
    "    raw = raw.drop(var, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#将数据分为训练集和测试集\n",
    "train_kobe = raw[pd.notnull(raw['shot_made_flag'])]\n",
    "train_label = train_kobe['shot_made_flag']\n",
    "train_kobe = train_kobe.drop('shot_made_flag', axis=1)\n",
    "\n",
    "test_kobe = raw[pd.isnull(raw['shot_made_flag'])]\n",
    "test_kobe = test_kobe.drop('shot_made_flag', axis=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('寻找随机森林分类器的的最佳树的数量...')\n",
    "min_score = 100000\n",
    "best_n = 0\n",
    "scores_n = []\n",
    "range_n = np.logspace(0, 2, num=10).astype(int)\n",
    "for n in range_n:\n",
    "    print('树的数量 : {0}'.format(n))\n",
    "    t1 = time.time()\n",
    "    rfc_score = 0.\n",
    "    rfc = RandomForestClassifier(n_estimators=n)\n",
    "    # use modern KFold API and iterate with .split()\n",
    "    kf = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "    for train_k, test_k in kf.split(train_kobe):\n",
    "        rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])\n",
    "        pred = rfc.predict(train_kobe.iloc[test_k])\n",
    "        rfc_score += log_loss(train_label.iloc[test_k], pred) / 10\n",
    "    scores_n.append(rfc_score)\n",
    "    if rfc_score < min_score:\n",
    "        min_score = rfc_score\n",
    "        best_n = n\n",
    "    t2 = time.time()\n",
    "    print('建造 {0} 颗树(耗时{1:.3f}秒)'.format(n, t2 - t1))\n",
    "print(\"最佳树的颗树为 : {0},得分为: {1}\".format(best_n,min_score))\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "print('寻找随机森林分类器的最佳树的最佳深度...')\n",
    "min_score = 100000\n",
    "best_m = 0\n",
    "scores_m = []\n",
    "range_m = np.logspace(0, 2, num=10).astype(int)\n",
    "for m in range_m:\n",
    "    print(\"树的最大的深度 : {0}\".format(m))\n",
    "    t1 = time.time()\n",
    "    rfc_score = 0.\n",
    "    rfc = RandomForestClassifier(max_depth=m, n_estimators=best_n)\n",
    "    # use modern KFold API and iterate with .split()\n",
    "    kf = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "    for train_k, test_k in kf.split(train_kobe):\n",
    "        rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])\n",
    "        pred = rfc.predict(train_kobe.iloc[test_k])\n",
    "        rfc_score += log_loss(train_label.iloc[test_k], pred) / 10\n",
    "    scores_m.append(rfc_score)\n",
    "    if rfc_score < min_score:\n",
    "        min_score = rfc_score\n",
    "        best_m = m\n",
    "    t2 = time.time()\n",
    "    print('树的最大深度为: {0}(耗时{1:.3f}秒)'.format(m, t2 - t1))\n",
    "print('最佳树的深度: {0},得分为：{1}'.format(best_m, min_score))\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(range_n, scores_n)\n",
    "plt.ylabel('score')\n",
    "plt.xlabel('number of trees')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(range_m, scores_m)\n",
    "plt.ylabel('score')\n",
    "plt.xlabel('max depth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afbba194",
   "metadata": {},
   "source": [
    "运行结果如下，说明当树的颗树为 100，并且深度为 12 的时候，损失函数最小，下面是具体的得分图示。\n",
    "\n",
    "下面我们用 100,12 这个参数训练模型，并且预测出 5000 个’shot_made_flag’的缺失值。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d02893",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from sklearn.ensemble import RandomForestClassifier  #导入随机森林分类器\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 导入数据\n",
    "filename = \"data.csv\"\n",
    "raw = pd.read_csv(filename)\n",
    "\n",
    "# 在删除列之前保存测试集的 shot_id 以便最后输出结果（避免 NameError）\n",
    "test_shot_id = raw.loc[pd.isnull(raw['shot_made_flag']), ['shot_id']].copy()\n",
    "\n",
    "# 删除shot_made_flag为空的数据项，并且命名为kobe用作训练\n",
    "kobe = raw[pd.notnull(raw['shot_made_flag'])].copy()\n",
    "\n",
    "# 计算 dist/angle/remaining_time 等（假设已经在前面的单元处理，如果没有这里补上）\n",
    "if 'dist' not in raw.columns:\n",
    "    raw['dist'] = np.sqrt(raw['loc_x']**2 + raw['loc_y']**2)\n",
    "if 'angle' not in raw.columns:\n",
    "    raw['angle'] = np.arctan2(raw['loc_y'], raw['loc_x']).apply(lambda a: a if a>=0 else a + 2*np.pi)\n",
    "if 'remaining_time' not in raw.columns:\n",
    "    raw['remaining_time'] = raw['minutes_remaining'] * 60 + raw['seconds_remaining']\n",
    "\n",
    "# 将数据分为训练集和测试集（使用 raw，因为它包含独热编码列）\n",
    "train_kobe = raw[pd.notnull(raw['shot_made_flag'])].copy()\n",
    "train_label = train_kobe['shot_made_flag'].astype(int)\n",
    "train_kobe = train_kobe.drop(['shot_made_flag'], axis=1)\n",
    "\n",
    "test_kobe = raw[pd.isnull(raw['shot_made_flag'])].copy()\n",
    "test_kobe = test_kobe.drop(['shot_made_flag'], axis=1)\n",
    "\n",
    "# 选择数值特征用于归一化（示例：dist, remaining_time）\n",
    "numeric_cols = [c for c in ['dist','remaining_time'] if c in train_kobe.columns]\n",
    "\n",
    "# 标准化数值特征\n",
    "scaler = StandardScaler()\n",
    "if numeric_cols:\n",
    "    scaler.fit(train_kobe[numeric_cols])\n",
    "    train_kobe[numeric_cols] = scaler.transform(train_kobe[numeric_cols])\n",
    "    test_kobe[numeric_cols] = scaler.transform(test_kobe[numeric_cols])\n",
    "\n",
    "# 如果存在对象列确保没有字符串残留（模型期望数值）\n",
    "train_kobe = train_kobe.fillna(0)\n",
    "test_kobe = test_kobe.fillna(0)\n",
    "\n",
    "# 训练模型并且用预测shot_made_flag的缺失值\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth=12, random_state=42)\n",
    "model.fit(train_kobe, train_label)\n",
    "predictions = model.predict(test_kobe)\n",
    "\n",
    "# ensure test_shot_id exists; if not, try to build it from raw; otherwise raise informative error\n",
    "if 'test_shot_id' not in globals():\n",
    "    if 'raw' in globals() and 'shot_id' in raw.columns:\n",
    "        test_shot_id = raw.loc[pd.isnull(raw['shot_made_flag']), ['shot_id']].copy()\n",
    "    else:\n",
    "        raise NameError(\"test_shot_id 未定义，且无法从 raw 构建。请先运行包含原始数据的单元。\")\n",
    "\n",
    "result = pd.DataFrame({'shot_id': test_shot_id['shot_id'].to_numpy(), 'shot_made_flag': predictions.astype(np.int32)})\n",
    "result.to_csv(\"result.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7afaa002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "shot_id",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "shot_made_flag",
         "rawType": "int32",
         "type": "integer"
        }
       ],
       "ref": "9b3e0b95-1e68-45b2-bd99-3b0df582357f",
       "rows": [
        [
         "0",
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         "2",
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         "4",
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shot_id</th>\n",
       "      <th>shot_made_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>17</td>\n",
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       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   shot_id  shot_made_flag\n",
       "0        1               0\n",
       "1        8               0\n",
       "2       17               1\n",
       "3       20               1\n",
       "4       33               0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79cd75b",
   "metadata": {},
   "source": [
    "这里给出了 5000 个缺失值。\n",
    "\n",
    "六、总结\n",
    "\n",
    "本篇文章主要用了机器学习的 sklearn 库，配合着 numpy，pandas，matplotlib 的技术路线，利用随机森林算法对科比生涯数据进行分析，对缺失值进行了预测。\n"
   ]
  }
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